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Nowcasting the COVID‐19 pandemic in Bavaria
Biometrical Journal ( IF 1.7 ) Pub Date : 2020-12-01 , DOI: 10.1002/bimj.202000112
Felix Günther 1, 2 , Andreas Bender 1 , Katharina Katz 3 , Helmut Küchenhoff 1 , Michael Höhle 4
Affiliation  

Abstract To assess the current dynamics of an epidemic, it is central to collect information on the daily number of newly diseased cases. This is especially important in real‐time surveillance, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred‐but‐not‐yet‐reported events. Here, we present a novel application of nowcasting to data on the current COVID‐19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time‐varying case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID‐19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID‐19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.

中文翻译:

巴伐利亚州 COVID-19 大流行的临近预报

摘要 为了评估流行病的当前动态,收集有关每日新增病例数的信息至关重要。这在实时监控中尤其重要,其目的是获得态势感知,例如,如果案件目前正在增加或减少。当仅查看每日报告的病例数时,疾病发作和病例报告之间的报告延迟妨碍了我们了解近期流行病动态的能力。临近预报可用于调整已发生但尚未报告的事件的每日病例数。在这里,我们展示了临近预报在巴伐利亚当前 COVID-19 大流行数据中的新应用。它基于分层贝叶斯模型,该模型考虑了报告延迟分布随时间的变化并与报告的工作日相关联。此外,我们提出了一种基于临近预报的预测来估计有效时变案例再现数 Re(t) 的方法。这些方法基于之前发表的工作,我们大大扩展并适应了当前的 COVID-19 病例临近预报任务。我们提供所开发方法的方法细节,根据当前大流行的数据说明结果,并根据巴伐利亚州 COVID-19 的合成和回顾性数据评估模型。我们的临近预报结果会报告给巴伐利亚卫生当局,并每天发布在网页上 (https://corona.stat.uni-muenchen.de/)。用于分析的代码和合成数据可从 https://github.com/FelixGuenther/nc_covid19_bavaria 获得,可用于调整我们对不同数据的方法。我们提出了一种基于临近预报的预测来估计有效时变案例再现数 Re(t) 的方法。这些方法基于之前发表的工作,我们大大扩展并适应了当前的 COVID-19 病例临近预报任务。我们提供所开发方法的方法细节,根据当前大流行的数据说明结果,并根据巴伐利亚州 COVID-19 的合成和回顾性数据评估模型。我们的临近预报结果会报告给巴伐利亚卫生当局,并每天发布在网页上 (https://corona.stat.uni-muenchen.de/)。用于分析的代码和合成数据可从 https://github.com/FelixGuenther/nc_covid19_bavaria 获得,可用于调整我们对不同数据的方法。我们提出了一种基于临近预报的预测来估计有效时变案例再现数 Re(t) 的方法。这些方法基于之前发表的工作,我们大大扩展并适应了当前的 COVID-19 病例临近预报任务。我们提供所开发方法的方法细节,根据当前大流行的数据说明结果,并根据巴伐利亚州 COVID-19 的合成和回顾性数据评估模型。我们的临近预报结果会报告给巴伐利亚卫生当局,并每天发布在网页上 (https://corona.stat.uni-muenchen.de/)。用于分析的代码和合成数据可从 https://github.com/FelixGuenther/nc_covid19_bavaria 获得,可用于调整我们对不同数据的方法。
更新日期:2020-12-01
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